Battery Performance Prediction

Abstract

A test system (110) for determining battery performance during development of a battery configuration in a test environment is proposed. The test system (110) comprises at least one communication interface (114) and at least one processing device (122). The test system (110) is configured for receiving operating data indicative of at least one test protocol via the communication interface (114). The test system (110) is configured for receiving battery performance input data via the communication interface (114). The processing device (122) is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data and on the operating data using at least one data driven model. The test system (110) is configured for providing at least parts of the predicted time series of the state variable.

1 . A test system for determining battery performance during development of a battery configuration in a test environment, the test system comprising at least one communication interface and at least one processing device,
 wherein the test system is configured for receiving operating data indicative of at least one test protocol via the communication interface,   wherein the test system is configured for receiving battery performance input data via the communication interface,   wherein the processing device is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data and on the operating data using at least one data driven model, and   wherein the test system is configured for providing at least parts of the predicted time series of the state variable.   
     
     
         2 . The test system according to  claim 1 , wherein the data driven model comprises at least one recurrent neural network such as at least one echo state network. 
     
     
         3 . The test system according to  claim 1 , wherein the state variable is derivable from at least one charge-discharge-curve, wherein the state variable is at least one variable selected from the group consisting of: discharge capacity; charge capacity; shape of charge-discharge curve; average voltage; open circuit voltage; differential capacity; coulombic efficiency; and internal resistance. 
     
     
         4 . The test system according to  claim 1 , wherein the operating data indicative of at least one test protocol comprises at least one sequence of different charge cycles and/or discharge cycles. 
     
     
         5 . The test system according to  claim 1 , wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. 
     
     
         6 . The test system according to  claim 1 , wherein the processing device is configured for using the test protocol as an input parameter for determining the predicted time series of the state variable with the data driven model, and/or wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for selecting one of the data driven models for determining the predicted time series of the state variable depending on the test protocol. 
     
     
         7 . The test system according to  claim 1 , wherein the processing device is configured for using the battery performance input data as input parameter for determining the predicted time series of the state variable with the data driven model. 
     
     
         8 . The test system according to  claim 1 , wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for analyzing the battery performance input data, wherein the analyzing comprises determining at least one material characteristic, wherein at least one of the data driven models is selected based on the material characteristic. 
     
     
         9 . The test system according to  claim 1 , wherein the battery performance input data comprises data generated in response to the test protocol. 
     
     
         10 . The test system according to  claim 1 , wherein the test protocol is predefined. 
     
     
         11 . The test system according to  claim 1 , wherein the data driven model was parametrized based on operating data indicative of the at least one test protocol and battery performance input data. 
     
     
         12 . The test system according to  claim 11 , wherein the data driven model uses knowledge of past and future charge-discharge-cycles following the at least one test protocol to predict future battery performance. 
     
     
         13 . The test system according to  claim 1 , wherein the data driven model has a time memory and/or the data driven model is a time dependent model 
     
     
         14 . The test system according to  claim 1 , wherein the test protocol comprises information about at least one battery performance test, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein in the battery performance test discharge-charge curves are determined for each cycle. 
     
     
         15 . (canceled) 
     
     
         16 . The test system according to  claim 1 , wherein the battery performance input data comprises one or more of information about discharge capacity; information charge capacity; information about shape of charge-discharge curve; information about average voltage; information about open circuit voltage; information about differential capacity; information about coulombic efficiency; and information about internal resistance. 
     
     
         17 . (canceled) 
     
     
         18 . A test rig configured for performing at least one battery performance test on at least one battery based on at least one test protocol, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein the battery performance test comprises determining of discharge-charge curves for each cycle, wherein the test rig comprises at least one communication interface configured for providing operating data indicative of the test protocol and battery performance input data to at least one test system according to  claim 1 . 
     
     
         19 . A method for determining at least one data driven model for determining battery performance during development of a battery configuration in a test environment, wherein the method comprises training the data driven model with at least one training data set, wherein the training data set comprises historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. 
     
     
         20 . The method according to  claim 19 , wherein the method comprises:
 generating at least one random dynamical reservoir;   determining at least one input unit and at least one output unit;   generating input-to-reservoir connections and output-to-reservoir connections;   selecting at least one input weight matrix and at least one reservoir weight matrix;   determining of output weights by training the data driven model with the training data, wherein the output weights are determined using regression analysis.   
     
     
         21 . A computer implemented method for determining battery performance during development of a battery configuration in a test environment, wherein in the method at least one test system according to  claim 1  is used, the method comprising:
 a) retrieving operating data indicative of at least one test protocol via at least one communication interface; 
 b) retrieving battery performance input data via the communication interface; 
 c) determining a predicted time series of a state variable indicative of battery performance based on the battery performance input data and on the operating data using a data driven model by using a processing device; 
 d) providing at least parts of the predicted time series of the state variable. 
 
     
     
         22 . A computer program for determining battery performance during development of a battery configuration in a test environment, configured for causing a computer or computer network to perform the method for determining battery performance during development of a battery configuration in a test environment according to the preceding claim, when executed on the computer or computer network, wherein the computer program is configured to perform at least steps a) to d) of the method for determining battery performance during development of a battery configuration in a test environment according to  claim 21  the preceding claim. 
     
     
         23 . (canceled) 
     
     
         24 . (canceled)